Hossain, Nadia

ABSTRACT TAR DNA‐binding protein 43 (TDP‐43) inclusions are defining pathological features of frontotemporal lobar degeneration (FTLD) but are also often observed in Alzheimer's disease ( AD ) and primary age‐related tauopathy (PART). TDP‐43 in AD is either associated with cognitive impairment or a protective‐life prolonging impact, and yet the localization, cellular and fragment characteristics of TDP‐43 need to be determined. We investigated the relationships between TDP‐43 volumetric inclusion burden in low likelihood AD (lAD) and definite PART by immunostaining against phosphorylated TDP‐43 (pTDP‐43), TDP‐43 C terminal (TDP‐C) and TDP‐43 N‐terminal (TDP‐N) fragments combined with 3D confocal imaging taken from eight regions: amygdala (basolateral [amygdala‐BL] and centromedial amygdala [amygdala‐CM]), the hippocampus (Cornu Ammonis [CA]‐1, CA2/3, CA4, dentate gyrus [DG] and subiculum [SUB]) and entorhinal cortex (ERC) and artificial intelligence (AI)–based segmentation via object recognition, reconstruction and quantification. We found amygdala‐CM in lAD and PART to have the overall greatest burden of pTDP‐43 whereas TDP‐N burden in amygdala‐BL of PART cases was greater than other TDP‐43 fragments. There was no difference in TDP‐43 burden in hippocampal subfields in PART. However, CA2/3 region showed greater pTDP‐43 burden while TDP‐N stood out in DG and SUB. Multiple comparisons among the groups revealed that TDP‐C was the only fragment showing differences among PART and lAD in CA2/3, DG and SUB regions. Overall, unbiased AI‐based volumetric burden analysis pipeline demonstrated unique fragment aggregation patterns in the neurodegenerative processes of PART and AD .

Abstract TAR DNA‐binding protein 43 (TDP‐43) inclusions are often associated with hyperphosphorylated tau, thus neurofibrillary tangles as the hallmark of Alzheimer's disease (AD) and primary age‐related tauopathy (PART). TDP‐43 in AD is associated with cognitive impairment, and while staging is known, the localization, cellular and inclusion characteristics of TDP‐43 are yet to be elucidated. We investigate relationships between TDP‐43 inclusions and the tangle maturation continuum in AD, PART, and co‐pathologies by multiplex immunostaining combined with artificial intelligence (AI)–based segmentation via object recognition, reconstruction, and quantification. We performed anti‐phosphorylated TDP‐43 immunofluorescence with phosphorylated tau labeling different stages and modifications of tangles (AT8, pS396, TauC3, MN423, GT38) in three controls, three cases with PART and TDP‐43 (PART‐TDP), five cases with high likelihood AD and TDP‐43 (AD‐TDP), and four cases of high likelihood AD with TDP‐43 and Lewy Body disease (AD‐TDP‐LBD). Confocal imaging was taken from eight regions: amygdala (amygdala‐BL and amygdala‐CM) and hippocampus (Cornu Ammonis (CA)‐1, CA2/3, CA4, dentate gyrus (DG), subiculum (SUB)), and entorhinal cortex (ERC) and quantified with AI segmentation to identify 3D spatial relations, thus the maturity of neurofibrillary tangle associated TDP‐43 (TAT) inclusions. TATs, which were either identified by pTDP‐43 and AT8 or pTDP‐43 and pS396 double positivity, were also investigated by Thioflavin S (ThioS) histochemistry. We found pS396 labeled mature TATs predominated in PART and AD in every region. Basolateral and centromedial amygdala displayed overall greatest number of pre‐TATs and mature TATs. Mature TATs were homogenously distributed among hippocampal subfields whereas CA4 and DG had the greatest mature TAT composition. ERC revealed closer numbers of pre‐TATs and mature TATs yet mature TATs predominated all groups. Unbiased AI‐based object identification, reconstruction, and TAT maturation analysis pipeline in conjunction with TDP‐43, tau, and ThioS multiplex immunostaining demonstrated unique aggregation and maturation patterns, highlighting region‐specific dynamics in the neurodegenerative processes of PART and AD.

Objective: Progressive apraxia of speech (PAOS) is a neurodegenerative syndrome characterized by impaired motor speech planning and programming, whereas behavioral variant frontotemporal dementia (bvFTD) is characterized by deviant behavioral (e.g., personality and social) features. Clinical and anatomic characteristics of bvFTD in the context of PAOS are understudied. Methods: We identified 12 participants with PAOS and features that were consistent with bvFTD at baseline or follow-up. Eleven completed a head MRI scan. We compared clinical features and anatomical patterns of atrophy in these 11 PAOS-bvFTD participants to 11 matched PAOS participants without bvFTD and 22 age- and sex-matched healthy controls. Statistical Parametric Mapping (SPM) was applied to visualize gray matter volume across both groups compared to controls and each other. Medians and 25th and 75th percentiles were assessed in patients and across groups; Fisher’s Exact Test and Mann–Whitney U tests were applied using BlueSky software, version 10.3.1-Pro. Results: As expected, PAOS-bvFTD participants performed worse on the Frontal Behavioral Inventory (median: 33/72 vs. 10/72), 20-item behavioral assessment scale (4.5/20 vs. 1.0/20), and the Neuropsychiatry inventory (4/36 vs. 1.5/36) compared to the PAOS group (p < 0.01 for all), with no differences in other demographic, neurological, or language tests. Seven of the eleven PAOS-bvFTD participants had bvFTD features develop within three years of symptom onset. The PAOS-bvFTD and PAOS groups showed volume loss in frontal lobe regions compared to controls, with PAOS-bvFTD participants having more prefrontal volume loss than PAOS participants. Conclusions: Behavioral features consistent with bvFTD can co-occur in patients with PAOS and are related to greater atrophy of the prefrontal cortex.